An overview of the study design and variables in the GangEpl (Gang Employment) dataset:

This is a long form dataset in which each participant was measured multiple times (indicated by the variable Session). We have 337 observations and 57 variables in total.

Within-subject design: Youth were randomly assigned to either BEP or usual services (US), and data were collected at each intervention session. Hypothesis: Increases in employment among BEP participants would be related to reductions in gang involvement over the course of treatment.

Between-subject design: Compare BEP and US participants at the 3 month and 6 month assessment periods. Hypothesis: BEP would lead to higher employment and less gang involvement than US, and that changes in employment would mediate the effect of treatment on gang involvement.

Variables:

Updates from Last Meeting

Replicate the graphs in the original paper

The plots I have replicated look roughly the same with the original plots, but they are still not exactly the same.

MLM Analysis with Specified Data Range

The summary of MLM analysis from last time demonstrated that the independent variable EmployHrs (Employment Hours) did not significantly predict the dependent variables Days_Gang (Gang Involvement), Gang_Mem (Gang Membership), and Gang_Activity (Gang Activity). It seems that none of level1 and level2 variables of EmployHrs are significantly predicting the Gang Activity. However, the random effects of level2 variable (Centered Variable of EmployHrs) are significant.

Since the MLM analysis with all participants over all time ranges seems not satisfying (nearly no significant predictions by the IVs), one explanation could be that some participants did not have enough data of time points so their variations were not satisfying for MLM analysis. Pariticipants with ID 10, 13, and 26 were excluded for too few data points. Besides, we excluded session longer than 6 months to further ensure that the dataset had enough variations. Thus, we decided to block off those participants and re-do the analysis. Besides, since this is a longitudinal analysis, we think probably the lad predictors would also contribute to the variation of the DVs. Thus we will include lag predictors in the model.

Gang Involvement

Here we can see the overall trend between Employ Hours and Gang Involvement is that for every average increase in centered employment hours the days with gangs of participants will decrease, although the decreasing trend looks not really obvious.

## Warning: Rows containing NAs were excluded from the model.

## Warning: Rows containing NAs were excluded from the model.

  • Results of Gaussain model with covariates Age and Months: It seems that Employment Hours is not significant for Gang Involvement at the within-person level, 95%CI [-0.05, 0.02]. Age is also not a significant predictor, 95%CI [-0.54, 1.00]. However, month December, 95%CI [-4.14, -0.56], and month September, 95%CI [-3.02, -0.24], have been found significant for Gang Involvement.

  • Results of Binomial model with covariates Age and Months (compute the probability of observing a specified number of “Successful involvement” for 7 days): It seems that Employment Hours is not significant for Gang Involvement at the within-person level, 95%CI [-0.07, 0.02]. Age is also not a significant predictor, 95%CI [-0.12, 1.77]. However, month December with 95%CI [-6.24, -1.75], month November with 95%CI [-4.27, -0.43], month Octorber with 95%CI [-3.55, -0.35], and month September with 95%CI [-4.44, -1.17], have been found significant for Gang Involvement.

  • Results of the model with lag predictor EmployHrs_lag1: It looks like the lag predictor of employment hours doesn’t significantly predicts Gang Involvement..

  • Here are some plots of within-person conditional effects of covariates on Gang Involvement. For both gaussian and binomial models, the plots indicate that days spent with gang for each participant became averagely decreasing as the employment hours increase.

Gang Activity

  • The overall trend between Employ Hours and Gang Activity is that for every average increase in employment hours the days with gangs of participants will decrease, even though the trend looks not linear.
## Warning: Rows containing NAs were excluded from the model.

  • Results of the model with covariate Age and months: It seems that Employment Hours is significant for Gang Activity at the within-person level, 95%CI [-0.16, -0.01]. Age is not a significant predictor. Month December with 95%CI [-9.44, -0.92], month January with 95%[-9.88, -0.29], and month November with 95%CI [-7.92,, -0.01], have been found significant for Gang Activity.

  • Results of the model with lag predictor EmployHrs_lag1: It looks like the lag predictor of Employment Hour does not significantly predict Gang Activity.

## Warning: Predictions are treated as continuous variables in
## 'conditional_effects' by default which is likely invalid for
## ordinal families. Please set 'categorical' to TRUE.

  • Here the point is that there is an obvious decreasing trend in Gang Activity for participants who spend more time in Employment. The within-person association between employment hours and gang acitivty is decreasing over time.

Gang Membership

## Warning: Rows containing NAs were excluded from the model.

## Warning: Rows containing NAs were excluded from the model.

  • Results of the gaussian model with covariate Age and months: It seems that Employ Hours is significant for Gang Membership at the within-person level, 95%CI [-0.08, 0.07]. Age is not a significant predictor for Gang Membership. None of the months has been found significant for Gang Membership.

  • Results of the binomial model with covariate Age and months (compute the probability of observing a specified number of “Successful Active Membership”): It seems that Employ Hours is not significant for Gang Membership at the within-person level, 95%CI [-0.07, 0.04]. Age is not a significant predictor for Gang Membership. Month August, December, July, November, October, and September have been found significant for Gang Membership.

  • Results of the model with lag predictor EmployHrs_lag1: It looks like the lag predictor of Employment Hour does not significantly predict Gang Activity.

  • The conditional effects of Employment Hours on Gang Membership looks really small because each line of within-person association is nearly close to horizontal.

Short Summary

We have some summary points for MLM analysis with covariates of month, age, and lag predictor of Employment Hours:

  1. It looks like Employment Hours does have a within-person level (centered person-mean significant effects on Gang Involvement, Gang Membership, and Gang Activity. The general trends for three paired relationship are both slightly decreasing.

  2. The covariate Age looks like not having significant effects on any of DVs, but some of months have significant effects on DVs, though the pattern of months is not clear to see.

  3. The lag predictor of Employment Hours wasn’t found significantly predicting three DVs.